Top Senior Data & Analytics Jobs

Leads U.S. execution of the enterprise data governance model, including ownership, stewardship, classification, data quality, governance forums, and standards adoption. Facilitates cross-functional domain working groups, drives training and communications, manages exceptions and risks, strengthens data governance networks, and supports data governance for acquisitions. Requires extensive data governance experience, stakeholder communication, and the ability to influence without direct authority.

Leads sales and commercial analytics to identify revenue opportunities, improve forecasting, measure performance, and support strategic decision-making. Develops Power BI dashboards, executive scorecards, reports, predictive analyses, and automated self-service tools. Partners with Commercial, Marketing, Finance, IT, Operations, and executive leaders to translate customer, product, market, and campaign data into actionable recommendations that improve revenue, retention, profitability, and business performance.

Build and maintain scalable batch and incremental data pipelines using Python, SQL, and Snowflake. Develop data warehouse models, transformations, automation, quality checks, lineage, alerting, and performance optimizations. Collaborate with analysts, data scientists, and application teams to deliver trusted datasets, while documenting pipelines, schemas, and data contracts.

Leads the design, delivery, and optimization of data architecture, pipelines, semantic layers, data quality frameworks, and Tableau BI platforms. Partners with stakeholders to manage requirements, project plans, sprint execution, monitoring, governance, and release delivery. Guides data engineers, manages team members, and supports AI-enabled analytics capabilities. Requires strong SQL, Spark, PySpark, SSIS, cloud data platform, CI/CD, Tableau infrastructure, and data engineering expertise.

Develop and execute database migration and integration solutions across relational and legacy systems. Analyze schemas, map source data to target databases, build SQL and Python ETL transformations, validate data quality, reconcile migrations, troubleshoot failures, and optimize large-scale processes. Use AI-assisted development tools responsibly to accelerate coding and documentation. Collaborate with implementation and engineering teams while maintaining reusable scripts, documentation, and reliable migration workflows.

Designs and maintains a scalable data platform supporting analytics, machine learning, and AI workloads. Builds tooling for reliable data pipelines, semantic layers, data agents, LLM applications, and evaluation systems. Ensures data quality, availability, observability, and reliability while establishing modeling, testing, and platform best practices. Collaborates with Data Science and Product Engineering, leads solutions from design through implementation, and contributes individually in a fast-paced startup environment.

Leads complex analytics and research initiatives, using advanced SQL, statistical methods, forecasting, data modeling, and BI tools to generate actionable insights. Partners with engineering and university stakeholders to resolve data quality issues, build data pipelines and products, create executive-ready visualizations, and influence strategic decisions. Mentors analysts and promotes organization-wide data visualization and analytics best practices.

Leads advanced analytics across the organization, owning MAU forecasting and growth models, experimentation standards, and causal inference methods. Establishes analytical best practices, reviews high-stakes analyses, partners with data and product teams, and scales recurring work through AI tooling. Mentors data professionals and translates complex statistical findings into actionable leadership recommendations.

Lead architecture and governance of the enterprise data platform, designing a unified data foundation across Snowflake, Databricks, OneLake and cloud services. Drive complex integrations, cross-platform governance, data cataloging, platform modernization, scalability and stability, and enable self-serve analytics and AI/ML for 5,000+ users.

Designs and optimizes data infrastructure, pipelines, models, and reporting environments supporting finance, treasury, audits, and securitization modeling. Partners with stakeholders to translate business needs into scalable data solutions, integrates data across systems, ensures data quality and lineage, and builds automated validation and monitoring processes. Requires advanced SQL, data architecture expertise, strong communication skills, and familiarity with financial reporting concepts and Snowflake.